Pangram recently published EditLens (ICLR 2026). Instead of just saying whether a piece of text is human or AI, it gives an estimate of how much of it an AI has edited. They open sourced two versions, one built on RoBERTa-large (an encoder model) and one on Llama-3.2-3B (a decoder).
\nThe interesting part of their results is on text model didnt saw during training. The Llama model barely drops (0.895 to 0.863 accuracy). The RoBERTa model falls off a cliff (0.881 to 0.695). It gives the reading that decoders generalize better.
\nThose two models differ in a lot more than encoder vs decoder. Llama model is about 8 times bigger, reads twice as much text at once [bigger context window] (1024 vs 512 tokens), was trained on much newer data, uses a different tokenizer and was fine-tuned in a different way.
\nEttin is a family of models where every encoder has a decoder equivalent. Same training data, same recipe, same tokenizer, same number of parameters. The only real difference is the architecture.
\nI fine-tuned encoder and decoder pairs at 17M, 32M, 68M, 150M and 400M parameters on the EditLens training set (available on hf), using the EditLens code and settings for everything. A run takes anywhere from a few minutes to half an hour on a single RTX 4090 GPU. I scored the same three test sets that Pangram used: text like the training data, Enron emails, and text written by Llama-3.3-70B.
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